Executive Summary
- Vector search excels at point-queries ('What is the server port?') but fails on global aggregation ('Summarize corporate risk').
- GraphRAG parses text chunks into structured entity nodes (people, orgs, servers) and directed relationship edges.
- Hierarchical Leiden community detection groups related entities into thematic clusters with pre-computed summaries.
- Multi-hop queries traverse graph paths (A -> B -> C) to discover non-obvious dependencies invisible to cosine similarity.
- Hybrid Graph+Vector pipelines deliver 40% higher accuracy on holistic enterprise summarization benchmarks.
The relational blindspot of vector similarity search
Vector search operates by calculating the distance between a query vector and isolated text chunk vectors. This works well when the answer is contained within a single paragraph: 'What is Acme Corp's cancellation policy?'
However, consider a global analytical question: 'Across all 50 subsidiary companies, which vendors provide critical software components, and what cybersecurity audits are pending for each?'
Because this information is scattered across hundreds of separate PDFs, no single text chunk contains high vector similarity to the prompt. Vector search retrieves 5 random vendor contracts and leaves the other 45 unmentioned.
GraphRAG solves this by converting unstructured text into an interconnected knowledge graph of entities and relationships.
The Aggregation Limit
Vector search is local search. Knowledge graph traversal is global reasoning. Complex enterprise inquiries demand global graph context.
The GraphRAG architecture: Extraction, Communities, and Traversal
The GraphRAG pipeline consists of three core computational phases:
1. Entity & Relationship Extraction: An LLM processes text chunks to extract entities (e.g. `[Acme Nordic, Organization]`, `[K8s Cluster, Infrastructure]`) and directed edges (`[Acme Nordic] --OPERATES--> [K8s Cluster]`).
2. Community Detection: Graph clustering algorithms (such as the Leiden algorithm) partition the network into hierarchical communities representing functional domains.
3. Community Summarization: LLMs generate synthesized summaries for each community cluster, allowing top-down global queries without scanning millions of raw tokens.
Traditional Vector RAG vs GraphRAG comparison
Evaluating query capabilities, extraction costs, and multi-hop reasoning across retrieval paradigms.
Retrieval paradigms compared
| Feature | Dimension | Traditional Vector RAG | GraphRAG (Knowledge Graph + Vectors) |
|---|---|---|---|
| Point Queries ('What is X?') | Fast & High Precision | Fast & High Precision | |
| Multi-Hop Traversal (A -> B -> C) | Fails (Cannot link across chunks) | Flawless (Explicit graph path traversal) | |
| Global Aggregation ('Summarize theme X') | Poor (Retrieves incomplete top-K subset) | Flawless (Hierarchical community summaries) | |
| Ingestion Compute Overhead | Low (Embedding models only) | Moderate to High (LLM entity extraction pipeline) | |
| Accuracy on Complex Audits | 58.2% | 94.6% (+36.4% gain) |
Multi-hop knowledge graph query TypeScript implementation
Below is a TypeScript implementation of a multi-hop GraphRAG query service traversing Cypher/Graph relations alongside vector similarity.
Hierarchical Leiden community summarization explained
The Leiden algorithm identifies natural clusters in the knowledge graph. Level 0 represents granular entity pairs, while Level 2 represents broad organizational divisions.
When an executive asks a broad question, the query engine reads pre-computed Level 2 community summaries, answering in 500ms without reading 10,000 raw documents.
Architecting hybrid Graph + Vector unified retrieval
Production architectures do not discard vector search; they unify both paradigms into a dual-engine architecture.
Vector search locates specific technical paragraphs, while graph traversal explores adjacent entity dependencies, providing the LLM with both local precision and global context.
Enterprise GraphRAG architecture checklist
Assess your knowledge management architecture against these GraphRAG standards.
GraphRAG readiness checklist
1Extraction & Graph Modeling
- Entity extraction pipelines extract normalized names, aliases, and types
- Graph databases (Neo4j / Amazon Neptune) store directed relational edges
- Deduplication algorithms merge duplicate entity representations
2Community Summaries & Retrieval
- Hierarchical community detection clusters entities into domain modules
- Community summary caches update asynchronously during document ingestion
- Hybrid query engines combine Cypher graph traversal with vector similarity